Recap: In Part 2 of this tutorial, we trained a model using a very simple version of Federated Learning. This required each data owner to trust the model owner to be able to see their gradients.
Description: In this tutorial, we'll show how to use the advanced aggregation tools from Part 3 to allow the weights to be aggregated by a trusted "secure worker" before the final resulting model is sent back to the model owner (us).
In this way, only the secure worker can see whose weights came from whom. We might be able to tell which parts of the model changed, but we do NOT know which worker (bob or alice) made which change, which creates a layer of privacy.
Authors:
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import torch
import syft as sy
import copy
hook = sy.TorchHook(torch)
from torch import nn, optim
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# create a couple workers
bob = sy.VirtualWorker(hook, id="bob")
alice = sy.VirtualWorker(hook, id="alice")
secure_worker = sy.VirtualWorker(hook, id="secure_worker")
# A Toy Dataset
data = torch.tensor([[0,0],[0,1],[1,0],[1,1.]], requires_grad=True)
target = torch.tensor([[0],[0],[1],[1.]], requires_grad=True)
# get pointers to training data on each worker by
# sending some training data to bob and alice
bobs_data = data[0:2].send(bob)
bobs_target = target[0:2].send(bob)
alices_data = data[2:].send(alice)
alices_target = target[2:].send(alice)
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# Iniitalize A Toy Model
model = nn.Linear(2,1)
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bobs_model = model.copy().send(bob)
alices_model = model.copy().send(alice)
bobs_opt = optim.SGD(params=bobs_model.parameters(),lr=0.1)
alices_opt = optim.SGD(params=alices_model.parameters(),lr=0.1)
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for i in range(10):
# Train Bob's Model
bobs_opt.zero_grad()
bobs_pred = bobs_model(bobs_data)
bobs_loss = ((bobs_pred - bobs_target)**2).sum()
bobs_loss.backward()
bobs_opt.step()
bobs_loss = bobs_loss.get().data
# Train Alice's Model
alices_opt.zero_grad()
alices_pred = alices_model(alices_data)
alices_loss = ((alices_pred - alices_target)**2).sum()
alices_loss.backward()
alices_opt.step()
alices_loss = alices_loss.get().data
print("Bob:" + str(bobs_loss) + " Alice:" + str(alices_loss))
Now that each data owner has a partially trained model, it's time to average them together in a secure way. We achieve this by instructing Alice and Bob to send their model to the secure (trusted) server.
Note that this use of our API means that each model is sent DIRECTLY to the secure_worker. We never see it.
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alices_model.move(secure_worker)
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bobs_model.move(secure_worker)
Finally, the last step for this training epoch is to average Bob and Alice's trained models together and then use this to set the values for our global "model".
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with torch.no_grad():
model.weight.set_(((alices_model.weight.data + bobs_model.weight.data) / 2).get())
model.bias.set_(((alices_model.bias.data + bobs_model.bias.data) / 2).get())
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iterations = 10
worker_iters = 5
for a_iter in range(iterations):
bobs_model = model.copy().send(bob)
alices_model = model.copy().send(alice)
bobs_opt = optim.SGD(params=bobs_model.parameters(),lr=0.1)
alices_opt = optim.SGD(params=alices_model.parameters(),lr=0.1)
for wi in range(worker_iters):
# Train Bob's Model
bobs_opt.zero_grad()
bobs_pred = bobs_model(bobs_data)
bobs_loss = ((bobs_pred - bobs_target)**2).sum()
bobs_loss.backward()
bobs_opt.step()
bobs_loss = bobs_loss.get().data
# Train Alice's Model
alices_opt.zero_grad()
alices_pred = alices_model(alices_data)
alices_loss = ((alices_pred - alices_target)**2).sum()
alices_loss.backward()
alices_opt.step()
alices_loss = alices_loss.get().data
alices_model.move(secure_worker)
bobs_model.move(secure_worker)
with torch.no_grad():
model.weight.set_(((alices_model.weight.data + bobs_model.weight.data) / 2).get())
model.bias.set_(((alices_model.bias.data + bobs_model.bias.data) / 2).get())
print("Bob:" + str(bobs_loss) + " Alice:" + str(alices_loss))
Lastly, we want to make sure that our resulting model learned correctly, so we'll evaluate it on a test dataset. In this toy problem, we'll use the original data, but in practice we'll want to use new data to understand how well the model generalizes to unseen examples.
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preds = model(data)
loss = ((preds - target) ** 2).sum()
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print(preds)
print(target)
print(loss.data)
In this toy example, the averaged model is underfitting relative to a plaintext model trained locally would behave, however we were able to train it without exposing each worker's training data. We were also able to aggregate the updated models from each worker on a trusted aggregator to prevent data leakage to the model owner.
In a future tutorial, we'll aim to do our trusted aggregation directly with the gradients, so that we can update the model with better gradient estimates and arrive at a stronger model.
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